To assimilate this characteristic within an RL framework, it is important to identify the sensitive information that might be gleaned through knowledge of individual components of the decision-making procedure.
Rewards received by an agent are an important descriptor of the task that needs to be completed; transition probabilities from an MDP representation reveal information about the consequence of taking a particular action; a trajectory of states visited by the agent can disclose tracking information [1]}.
Previous research has established that the rewards are the most vulnerable component [2]}, [3]}.
Consequently, we propose a method to incorporate differential privacy into risk-sensitive reinforcement learning, to ensure that rewards which are `sufficiently close' to each other will be indistinguishable.
